{"title":"设计用于多电平逆变器建模的混合元逻辑优化器","authors":"V. Bharath Choudary, A. Kavithamani","doi":"10.1166/jno.2024.3607","DOIUrl":null,"url":null,"abstract":"Meta-heuristic (MH) algorithms have significantly impacted optimization in several technical domains. These algorithms must be implemented in hardware for several technical applications. Hence their performance is essential. Multilayer inverter failure detection is widely applied in\n High Voltage DC (HVDC) conduction and Industrialized Drives. It uses various meta-heuristic techniques and a NN (Neural Network) as the DM (Decision-Making) mechanism. After the network has been trained for various failure scenarios in the multilevel inverter, the weight and bias parameters\n are optimized using a MH optimizer to compare the model’s performance. The output of a Multilevel Inverter (ML9LI) supplied by the system is approximated and inferred using a MATLAB-based approach. Features gained from the multi-level inverter, such as positive, negative, and zero sequence\n voltage and the THD of the output voltage, boost the FD (Fault Detection) ability when using a renewable energy-based power generation system as the basis for the inverter. Particle Swarm Optimization (PSO) and Firefly optimization (FO) are hybridized to form Multi-level Inverter (MLI)-based\n optimization methods are employed.","PeriodicalId":16446,"journal":{"name":"Journal of Nanoelectronics and Optoelectronics","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of a Hybrid Meta-Heuristic Optimizer for Modelling a Multi-Level Inverter\",\"authors\":\"V. Bharath Choudary, A. Kavithamani\",\"doi\":\"10.1166/jno.2024.3607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Meta-heuristic (MH) algorithms have significantly impacted optimization in several technical domains. These algorithms must be implemented in hardware for several technical applications. Hence their performance is essential. Multilayer inverter failure detection is widely applied in\\n High Voltage DC (HVDC) conduction and Industrialized Drives. It uses various meta-heuristic techniques and a NN (Neural Network) as the DM (Decision-Making) mechanism. After the network has been trained for various failure scenarios in the multilevel inverter, the weight and bias parameters\\n are optimized using a MH optimizer to compare the model’s performance. The output of a Multilevel Inverter (ML9LI) supplied by the system is approximated and inferred using a MATLAB-based approach. Features gained from the multi-level inverter, such as positive, negative, and zero sequence\\n voltage and the THD of the output voltage, boost the FD (Fault Detection) ability when using a renewable energy-based power generation system as the basis for the inverter. Particle Swarm Optimization (PSO) and Firefly optimization (FO) are hybridized to form Multi-level Inverter (MLI)-based\\n optimization methods are employed.\",\"PeriodicalId\":16446,\"journal\":{\"name\":\"Journal of Nanoelectronics and Optoelectronics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Nanoelectronics and Optoelectronics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1166/jno.2024.3607\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nanoelectronics and Optoelectronics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1166/jno.2024.3607","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Design of a Hybrid Meta-Heuristic Optimizer for Modelling a Multi-Level Inverter
Meta-heuristic (MH) algorithms have significantly impacted optimization in several technical domains. These algorithms must be implemented in hardware for several technical applications. Hence their performance is essential. Multilayer inverter failure detection is widely applied in
High Voltage DC (HVDC) conduction and Industrialized Drives. It uses various meta-heuristic techniques and a NN (Neural Network) as the DM (Decision-Making) mechanism. After the network has been trained for various failure scenarios in the multilevel inverter, the weight and bias parameters
are optimized using a MH optimizer to compare the model’s performance. The output of a Multilevel Inverter (ML9LI) supplied by the system is approximated and inferred using a MATLAB-based approach. Features gained from the multi-level inverter, such as positive, negative, and zero sequence
voltage and the THD of the output voltage, boost the FD (Fault Detection) ability when using a renewable energy-based power generation system as the basis for the inverter. Particle Swarm Optimization (PSO) and Firefly optimization (FO) are hybridized to form Multi-level Inverter (MLI)-based
optimization methods are employed.